A Genetic Approach to Statistical Disclosure Control

Smith, James E.; Clark, Alistair; Staggemeier, Andrea T.; Serpell, Martin

doi:10.1109/tevc.2011.2159271

Cited by 10 publications

(7 citation statements)

References 17 publications

(16 reference statements)

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“…After finding the significant differences between the k values, it is important to find the best k value. A nonparametric test (Wilcoxon signed rank test) [18] is performed to find the best k value among the considered k values. As a null hypothesis, it is assumed that there is no significant difference between paired k values, whereas the alternate hypothesis is that there is a significant difference between the paired k values at the 5% significance level.…”

Section: Resultsmentioning

confidence: 99%

A problem approximation surrogate model (PASM) for fitness approximation in optimizing the quantization table for the JPEG baseline algorithm

Balasubramanian¹,

Karpagam²

2016

Turk J Elec Eng & Comp Sci

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Abstract:The quantization table in the baseline Joint Photographic Experts Group (JPEG) algorithm plays an important role in compression/quality trade-off. Hence the detection of the optimal quantization table is viewed as an optimization problem. The genetic algorithm (GA) is an attractive optimization tool by many researchers for this application due to its ability in dealing with complex problems. In spite of its advantages, the GA requires more computation time to achieve an optimal solution if it has an expensive fitness evaluation. This paper proposes a problem approximation surrogate model (PASM) for fitness approximation to assist the GA in optimizing the quantization table for a target bits per pixel. This proposal reduces the computational time of the GA without any loss in performance. The PASM uses an image block clustering process and an indirect evaluation method to approximate the fitness value. The number of clusters in the clustering process may influence the performance of the PASM. A performance analysis with different number of clusters has been done and a suitable cluster number is identified with the help of measuring criteria such as mean squared difference, correct selection, potentially correct selection, and rank correlation. In addition, the results acquired from these measuring criteria are confirmed using statistical hypothesis tests such as Friedman's ANOVA and Wilcoxon signed rank. The PASM with suitable cluster number has been tested in a classical genetic algorithm and knowledge based genetic algorithm. Several benchmark images with different complexity levels have been examined in three different target bits per pixel to validate the performance of the PASM. The results proved that the PASM guarantees better results in terms of peak signal-to-noise ratio with a reduction in computational time.

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Section: Resultsmentioning

confidence: 99%

A problem approximation surrogate model (PASM) for fitness approximation in optimizing the quantization table for the JPEG baseline algorithm

Balasubramanian¹,

Karpagam²

2016

Turk J Elec Eng & Comp Sci

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“…This alication of efficient network optimization algorithms, such as those for st network flows, or shortest-paths [1]. This fact was originally noticed has been extensively used in other works [4,5,6,9,14,23,29].…”

Section: Safety Of Suppressed Tabular Datamentioning

confidence: 93%

“…The goal of CSP is to determine a set of suppressed cells that ensure sufficient uncertainty of the values of sensitive cells under the presence of linear relations concerning marginal sums in the table. Since CSP is known to be NP-hard, many researchers have previously developed efficient approximate or heuristics-based algorithms [2][3][4]. Particularly, the algorithm [1] based on the technique of Benders decomposition [5] has been adopted for tools for statistical disclosure control (SDC), such as τ-ARGUS [6], SDCLink [7] and sdcTable [8], because that algorithm guarantees to produce optimal solutions while running efficiently in many realistic situations.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic Matching Attacks on Optimally Suppressed Tabular Data

Minami

Abe

2019

Algorithms

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The objective of the cell suppression problem (CSP) is to protect sensitive cell values in tabular data under the presence of linear relations concerning marginal sums. Previous algorithms for solving CSPs ensure that every sensitive cell has enough uncertainty on its values based on the interval width of all possible values. However, we find that every deterministic CSP algorithm is vulnerable to an adversary who possesses the knowledge of that algorithm. We devise a matching attack scheme that narrows down the ranges of sensitive cell values by matching the suppression pattern of an original table with that of each candidate table. Our experiments show that actual ranges of sensitive cell values are significantly narrower than those assumed by the previous CSP algorithms.

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“…Since then much work [18] [19] [20] has gone into improving the performance of the GA so that it wound find solutions that reduced the over-suppression of the incremental LP model.…”

Section: A Permutation Problemmentioning

confidence: 99%

Initial application of ant colony optimisation to statistical disclosure control

Serpell

Smith

2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

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In this paper Ant Colony Optimisation (ACO) is applied in the field of Statistical Disclosure Control (SDC) for the first time. It has been applied to a permutation problem found in Cell Suppression. ACO has successfully improved the suppression patterns created to protect published statistical tables but when compared to using the Genetic Algorithm (GA) it has not performed as well. It has however performed well enough to merit further investigation into its use in SDC. In particular research into how to construct a distance matrix for the Cell Suppression Problem (CSP) may both improve the performance of ACO when it is applied in that field and provide further insight into SDC.

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A Genetic Approach to Statistical Disclosure Control

Cited by 10 publications

References 17 publications

A problem approximation surrogate model (PASM) for fitness approximation in optimizing the quantization table for the JPEG baseline algorithm

A problem approximation surrogate model (PASM) for fitness approximation in optimizing the quantization table for the JPEG baseline algorithm

Algorithmic Matching Attacks on Optimally Suppressed Tabular Data

Initial application of ant colony optimisation to statistical disclosure control

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